Artificial Intelligence and Robotics Commons^™

Ethical Aspects Of Utilising Artificial Intelligence In Clinical Settings, Jeffrey Byrnes, Michael Robinson

Philosophy Faculty Articles and Research

In response to recent proposals to utilize artificial intelligence (AI) to automate ethics consultations in healthcare, we raise two main problems for the prospect of having healthcare professionals rely on AI-driven programs to provide ethical guidance in clinical matters. The first cause for concern is that, because these programs would effectively function like black boxes, this approach seems to preclude the kind of transparency that would allow clinical staff to explain and justify treatment decisions to patients, fellow caregivers, and those tasked with providing oversight. The other main problem is that the kind of authority that would need to be …

Transparency And Authority Concerns With Using Ai To Make Ethical Recommendations In Clinical Settings, Jeffrey Byrnes, Michael Robinson

Philosophy Faculty Articles and Research

In response to recent proposals to utilize artificial intelligence (AI) to automate ethics consultations in healthcare, we raise two main problems for the prospect of having healthcare professionals rely on AI-driven programs to provide ethical guidance in clinical matters. The first cause for concern is that, because these programs would effectively function like black boxes, this approach seems to preclude the kind of transparency that would allow clinical staff to explain and justify treatment decisions to patients, fellow caregivers, and those tasked with providing oversight. The other main problem is that the kind of authority that would need to be …

Feasibility Of Large Language Models For Ceus Li-Rads Categorization Of Small Liver Nodules In Patients At Risk For Hepatocellular Carcinoma, Jiayan Huang, Rui Yang, Xiaotong Huang, Keyu Zeng, Yan Liu, Jun Luo, Andrej Lyshchik, Qiang Lu

Department of Radiology Faculty Papers

BACKGROUND: Large language models (LLMs) offer opportunities to enhance radiological applications, but their performance in handling complex tasks remains insufficiently investigated.

PURPOSE: To evaluate the performance of LLMs integrated with Contrast-enhanced Ultrasound Liver Imaging Reporting and Data System (CEUS LI-RADS) in diagnosing small (≤20mm) hepatocellular carcinoma (sHCC) in high-risk patients.

MATERIALS AND METHODS: From November 2014 to December 2023, high-risk HCC patients with untreated small (≤20mm) focal liver lesions (sFLLs), were included in this retrospective study. ChatGPT-4.0, ChatGPT-4o, ChatGPT-4o mini, and Google Gemini were integrated with imaging features from structured CEUS LI-RADS reports to assess their diagnostic performance for sHCC. …

Artificial Intelligence-Based Methodologies For Early Diagnostic Precision And Personalized Therapeutic Strategies In Neuro-Ophthalmic And Neurodegenerative Pathologies, Rahul Kumar, Ethan Waisberg, Joshua Ong, Phani Paladugu, Dylan Amiri, Jeremy Saintyl, Jahnavi Yelamanchi, Robert Nahouraii, Ram Jagadeesan, Alireza Tavakkoli

SKMC Student Presentations and Publications

Advancements in neuroimaging, particularly diffusion magnetic resonance imaging (MRI) techniques and molecular imaging with positron emission tomography (PET), have significantly enhanced the early detection of biomarkers in neurodegenerative and neuro-ophthalmic disorders. These include Alzheimer's disease, Parkinson's disease, multiple sclerosis, neuromyelitis optica, and myelin oligodendrocyte glycoprotein antibody disease. This review highlights the transformative role of advanced diffusion MRI techniques-Neurite Orientation Dispersion and Density Imaging and Diffusion Kurtosis Imaging-in identifying subtle microstructural changes in the brain and visual pathways that precede clinical symptoms. When integrated with artificial intelligence (AI) algorithms, these techniques achieve unprecedented diagnostic precision, facilitating early detection of neurodegeneration and …

Alphafold2-Based Characterization Of Apo And Holo Protein Structures And Conformational Ensembles Using Randomized Alanine Sequence Scanning Adaptation: Capturing Shared Signature Dynamics And Ligand-Induced Conformational Changes, Nishank Raisinghani, Vedant Parikh, Brandon Foley, Gennady M. Verkhivker

Mathematics, Physics, and Computer Science Faculty Articles and Research

Proteins often exist in multiple conformational states, influenced by the binding of ligands or substrates. The study of these states, particularly the apo (unbound) and holo (ligand-bound) forms, is crucial for understanding protein function, dynamics, and interactions. In the current study, we use AlphaFold2, which combines randomized alanine sequence masking with shallow multiple sequence alignment subsampling to expand the conformational diversity of the predicted structural ensembles and capture conformational changes between apo and holo protein forms. Using several well-established datasets of structurally diverse apo-holo protein pairs, the proposed approach enables robust predictions of apo and holo structures and conformational ensembles, …

A Machine Learning Approach To Multifactorial Modeling Of Episodic Memory Performance, Evan Fletcher, Marianne Chanti-Ketterl, Emily Hokett, Yi Lor, Umesh Venkatesan, Paola Gilsanz, Rachel Whitmer, Ruijia Chen, Kristen George, Zvinka Zlatar

Moss-Magee Rehabilitation Papers

BACKGROUND: Neurocognitive health is influenced by multiple modifiable and non-modifiable lifestyle factors. Machine learning tools offer a promising approach to better understand complex models of cognitive function. We used extreme gradient boosting (XG Boost), an algorithm of decision-tree modeling, to analyze the association between 15 late-life lifestyle and demographic factors with episodic memory performance. METHOD: Our dataset consisted of 2247 participants from the KHANDLE and STAR cohorts. Participants included 841 men and 1406 women, an ethnoracial diversity of 413 Asian, 987 Black, 349 Latinx, and 496 White adults with age range 54-90 (mean = 74). XG Boost models of continuous …

Exploring Post-Covid-19 Health Effects And Features With Advanced Machine Learning Techniques, Muhammad N. Islam, Md S. Islam, Nahid H. Shourav, Iftiaqur Rahman, Faiz A. Faisal, Md M. Islam, Iqbal H. Sarker

Research outputs 2022 to 2026

COVID-19 is an infectious respiratory disease that has had a significant impact, resulting in a range of outcomes including recovery, continued health issues, and the loss of life. Among those who have recovered, many experience negative health effects, particularly influenced by demographic factors such as gender and age, as well as physiological and neurological factors like sleep patterns, emotional states, anxiety, and memory. This research aims to explore various health factors affecting different demographic profiles and establish significant correlations among physiological and neurological factors in the post-COVID-19 state. To achieve these objectives, we have identified the post-COVID-19 health factors and …

Addressing Ethical Issues In Healthcare Artificial Intelligence Using A Lifecycle-Informed Process, Benjamin X Collins, Jean-Christophe Bélisle-Pipon, Barbara J Evans, Kadija Ferryman, Xiaoqian Jiang, Camille Nebeker, Laurie Novak, Kirk Roberts, Martin Were, Zhijun Yin, Vardit Ravitsky, Joseph Coco, Rachele Hendricks-Sturrup, Ishan Williams, Ellen W Clayton, Bradley A Malin, Bridge2ai Ethics And Trustworthy Ai Working Group

Faculty, Staff and Student Publications

OBJECTIVES: Artificial intelligence (AI) proceeds through an iterative and evaluative process of development, use, and refinement which may be characterized as a lifecycle. Within this context, stakeholders can vary in their interests and perceptions of the ethical issues associated with this rapidly evolving technology in ways that can fail to identify and avert adverse outcomes. Identifying issues throughout the AI lifecycle in a systematic manner can facilitate better-informed ethical deliberation.

MATERIALS AND METHODS: We analyzed existing lifecycles from within the current literature for ethical issues of AI in healthcare to identify themes, which we relied upon to create a lifecycle …

Artificial Intelligence And Machine Learning In Cancer Pain: A Systematic Review, Vivian Salama, Brandon Godinich, Yimin Geng, Laia Humbert-Vidan, Laura Maule, Kareem A Wahid, Mohamed A Naser, Renjie He, Abdallah S R Mohamed, Clifton D Fuller, Amy C Moreno

Faculty, Staff and Student Publications

Background/objectives: Pain is a challenging multifaceted symptom reported by most cancer patients. This systematic review aims to explore applications of artificial intelligence/machine learning (AI/ML) in predicting pain-related outcomes and pain management in cancer.

Methods: A comprehensive search of Ovid MEDLINE, EMBASE and Web of Science databases was conducted using terms: "Cancer," "Pain," "Pain Management," "Analgesics," "Artificial Intelligence," "Machine Learning," and "Neural Networks" published up to September 7, 2023. AI/ML models, their validation and performance were summarized. Quality assessment was conducted using PROBAST risk-of-bias andadherence to TRIPOD guidelines.

Results: Forty four studies from 2006 to 2023 were included. Nineteen studies used …

Predicting And Monitoring Immune Checkpoint Inhibitor Therapy Using Artificial Intelligence In Pancreatic Cancer, Guangbo Yu, Zigeng Zhang, Aydin Eresen, Qiaoming Hou, Farideh Amirrad, Sha Webster, Surya M. Nauli, Vahid Yaghmai, Zhuoli Zhang

Pharmacy Faculty Articles and Research

Pancreatic cancer remains one of the most lethal cancers, primarily due to its late diagnosis and limited treatment options. This review examines the challenges and potential of using immunotherapy to treat pancreatic cancer, highlighting the role of artificial intelligence (AI) as a promising tool to enhance early detection and monitor the effectiveness of these therapies. By synthesizing recent advancements and identifying gaps in the current research, this review aims to provide a comprehensive overview of how AI and immunotherapy can be integrated to develop more personalized and effective treatment strategies. The insights from this review may guide future research efforts …

On The Role Of Prediction In Streaming Hierarchical Learning, Ramy Mounir

USF Tampa Graduate Theses and Dissertations

In today's world, AI systems need to make sense of large amounts of data as it unfolds in real-time, whether it's a video from surveillance and monitoring cameras, streams of egocentric footage, or sequences in other domains such as text or audio. The ability to break these continuous data streams into meaningful events, discover nested structures, and predict what might happen next at different levels of abstraction is crucial for applications ranging from passive surveillance systems to sensory-motor autonomous learning. However, most existing models rely heavily on large, annotated datasets with fixed data distributions and offline epoch-based training, which makes …

Left, Then Right Internal Carotid Artery Dissection: A Case Report, Jeffrey M. Kalczynski, John Douds, Michael E. Silverman

SKMC Student Presentations and Publications

INTRODUCTION: We present a unique case of a patient who presented to the emergency department with stroke-like symptoms found to have a spontaneous, left-sided internal carotid artery dissection (ICAD).

CASE REPORT: The patient was treated successfully with thrombectomy and subsequently developed contralateral symptoms caused by a right-sided ICAD. This was managed with a second contra-lateral thrombectomy. The patient's course was complicated by persistent and mild hypotension, postulated to be secondary to bilateral carotid baroreceptor trauma from the dissections.

CONCLUSION: This case highlights the importance of close neurological monitoring for patients, preferably in a neurologic critical care setting, during and after …

Toward A Responsible Future: Recommendations For Ai-Enabled Clinical Decision Support, Steven Labkoff, Bilikis Oladimeji, Joseph Kannry, Anthony Solomonides, Russell Leftwich, Eileen Koski, Amanda L Joseph, Monica Lopez-Gonzalez, Lee A Fleisher, Kimberly Nolen, Sayon Dutta, Deborah R Levy, Amy Price, Paul J Barr, Jonathan D Hron, Baihan Lin, Gyana Srivastava, Nuria Pastor, Unai Sanchez Luque, Tien Thi Thuy Bui, Reva Singh, Tayler Williams, Mark G Weiner, Tristan Naumann, Dean F Sittig, Gretchen Purcell Jackson, Yuri Quintana

Faculty, Staff and Student Publications

BACKGROUND: Integrating artificial intelligence (AI) in healthcare settings has the potential to benefit clinical decision-making. Addressing challenges such as ensuring trustworthiness, mitigating bias, and maintaining safety is paramount. The lack of established methodologies for pre- and post-deployment evaluation of AI tools regarding crucial attributes such as transparency, performance monitoring, and adverse event reporting makes this situation challenging.

OBJECTIVES: This paper aims to make practical suggestions for creating methods, rules, and guidelines to ensure that the development, testing, supervision, and use of AI in clinical decision support (CDS) systems are done well and safely for patients.

MATERIALS AND METHODS: In May …

Against The Substitutive Approach To Ai In Healthcare, Rand Hirmiz

Research Collection School of Social Sciences

Paul Bloom has famously argued against the need for empathy in clinicians, while Sally Dalton-Brown has argued that AI need not be capable of empathy to be a good carer. In this paper, the capacity for AI to substitute for human clinicians is assessed from a bioethical perspective, primarily through the evaluation of the arguments put forth by Dalton-Brown and Bloom concerning empathy in healthcare. In opposition to both Bloom and Dalton-Brown, this paper argues that (1) empathy is essential to providing good care or deep care (that is, care that goes beyond the mere fulfilment of medical tasks), (2) …

Regulating Adaptive Medical Artificial Intelligence: Can Less Oversight Lead To Greater Compliance?, Jiayi Lai, Liang Xu, Xin Fang, Tinglong Dai

Research Collection Lee Kong Chian School Of Business

As of June 2024, the U.S. Food and Drug Administration (FDA) has approved 950 medical artificial intelligence (AI) devices. The current regulatory framework freezes AI algorithms after approval, requiring new submissions for updates to ensure compliance with Good Machine Learning Practices (GMLP). This approach imposes a significant administrative burden, while hindering the ability of AI algorithms to learn from new data. To address these challenges, the FDA has explored a novel pathway known as Predetermined Change Control Plans (PCCP), allowing developers to outline future changes during initial submissions and exempting approved changes from regulatory review. Yet, the impact of this …

National Use Of Artificial Intelligence For Eye Screening In Singapore, Dinesh Visva Gunasekeran, Steven Miller, Wynne Hsu, Mong Li, Tym Hon Wong, Mun Tuck Lee, Ecosse Lamoureau, Daniel Shu Wei Ting, Gavin Siew Wei Tan, Tien-Yin Wong

Research Collection School Of Computing and Information Systems

Diabetes is a major health care challenge, affecting 10% of the global population. One third of patients with diabetes have an ocular complication known as diabetic retinopathy (DR). DR progression to manifestations such as vision-threatening diabetic retinopathy (VTDR) remains the leading cause of blindness in working-aged adults. Yearly DR screening is a universally recommended practice in primary care settings for patients with diabetes, but it is often difficult to implement due to a lack of staffing and screening capacity in primary care. This case study highlights our experience with developing a medical artificial intelligence (AI) software-as-a-medical-device (SaMD) solution for DR …

Interpreting The Biological Effects Of Protons As A Function Of Physical Quantity: Linear Energy Transfer Or Microdosimetric Lineal Energy Spectrum?, Fada Guan, Lawrence Bronk, Matthew Kerr, Yuting Li, Leslie A Braby, Mary Sobieski, Xiaochun Wang, Xiaodong Zhang, Clifford Stephan, David R Grosshans, Radhe Mohan

Faculty, Staff and Student Publications

The choice of appropriate physical quantities to characterize the biological effects of ionizing radiation has evolved over time coupled with advances in scientific understanding. The basic hypothesis in radiation dosimetry is that the energy deposited by ionizing radiation initiates all the consequences of exposure in a biological sample (e.g., DNA damage, reproductive cell death). Physical quantities defined to characterize energy deposition have included dose, a measure of the mean energy imparted per unit mass of the target, and linear energy transfer (LET), a measure of the mean energy deposition per unit distance that charged particles traverse in a medium. The …

Artificial Intelligence And Machine Learning In Ocular Oncology, Retinoblastoma (Armor): Experience With A Multiracial Cohort, Vijitha S. Vempuluru, Rajiv Viriyala, Virinchi Ayyagari, Komal Bakal, Patanjali Bhamidipati, Krishna Krishore Dhara, Sandor R. Ferenczy, Carol L. Shields, Swathi Kaliki

Wills Eye Hospital Papers

Background: The color variation in fundus images from differences in melanin concentrations across races can affect the accuracy of artificial intelligence and machine learning (AI/ML) models. Hence, we studied the performance of our AI model (with proven efficacy in an Asian-Indian cohort) in a multiracial cohort for detecting and classifying intraocular RB (iRB). Methods: Retrospective observational study. Results: Of 210 eyes, 153 (73%) belonged to White, 37 (18%) to African American, 9 (4%) to Asian, 6 (3%) to Hispanic races, based on the U.S. Office of Management and Budget's Statistical Policy Directive No.15 and 5 (2%) had no reported race. …

The Psychological Impacts Of Algorithmic And Ai-Driven Social Media On Teenagers: A Call To Action, Sunil Arora, Sahil Arora, John Hastings

Research & Publications

This study investigates the meta-issues surrounding social media, which, while theoretically designed to enhance social interactions and improve our social lives by facilitating the sharing of personal experiences and life events, often results in adverse psychological impacts. Our investigation reveals a paradoxical outcome: rather than fostering closer relationships and improving social lives, the algorithms and structures that underlie social media platforms inadvertently contribute to a profound psychological impact on individuals, influencing them in unforeseen ways. This phenomenon is particularly pronounced among teenagers, who are disproportionately affected by curated online personas, peer pressure to present a perfect digital image, and the …

"Deep Learning For Microscope Image Denoising", Nasreen Buhn, Sriya Adunur, Guy Hagen, Jonathan Ventura

College of Engineering Summer Undergraduate Research Program

In order to avoid damaging live cells, optical microscope imaging must be conducted under low-excitation light intensity and/or short exposure times, resulting in low signal-to-noise ratios (SNR). Deep learning methods offer an effective solution for removing microscope noise, utilizing algorithms that are able to reconstruct finer features in low SNR images. This research explores the denoising capability of several deep learning methods based on PSNR and SSIM. Tested methods include traditional approaches (BMED), supervised learning (CARE and Restormer), and unsupervised methods (Noise2Fast, N2V, SSD-Unsupervised, and SASSID). The Restormer model, which employs an encoder-decoder transformer architecture and progressive learning, stood out …

Equipping Future Physicians With Artificial Intelligence Competencies Through Student Associations, Spencer Hopson, Carson Mildon, Kyle Hassard, Paul Urie, Dennis Della Corte

Faculty Publications

Advances in artificial intelligence (AI) in the medical sector necessitate the development of AI literacy among future physicians. This article explores the pioneering efforts of the AI in Medicine Association (AIM) at Brigham Young University, which offers a framework for undergraduate pre-medical students to gain hands-on experience, receive principled education, explore ethical considerations, and learn appraisal of AI models. By supplementing formal, university-organized pre-medical education with a student-led, faculty-supported introduction to AI through an extracurricular academic association, AIM alleviates apprehensions regarding AI in medicine early and empowers students preparing for medical school to navigate the evolving landscape of AI in …

A Guideline For Open-Source Tools To Make Medical Imaging Data Ready For Artificial Intelligence Applications: A Society Of Imaging Informatics In Medicine (Siim) Survey, Sanaz Vahdati, Bardia Khosravi, Elham Mahmoudi, Kuan Zhang, Pouria Rouzrokh, Shahriar Faghani, Mana Moassefi, Aylin Tahmasebi, Katherine Andriole, Peter Chang, Keyvan Farahani, Mona Flores, Les Folio, Sina Houshmand, Maryellen Giger, Judy Gichoya, Bradley Erickson

Department of Radiology Faculty Papers

In recent years, the role of Artificial Intelligence (AI) in medical imaging has become increasingly prominent, with the majority of AI applications approved by the FDA being in imaging and radiology in 2023. The surge in AI model development to tackle clinical challenges underscores the necessity for preparing high-quality medical imaging data. Proper data preparation is crucial as it fosters the creation of standardized and reproducible AI models while minimizing biases. Data curation transforms raw data into a valuable, organized, and dependable resource and is a fundamental process to the success of machine learning and analytical projects. Considering the plethora …

Exploring Conversations Between A Practitioner And A Person With Dementia, Kotaro Hara, Rosiana Natalie, Wei Soon Cheong, Jingjing Gu, Qianli Xu

Research Collection School Of Computing and Information Systems

In social service centers, practitioners engage in conversations with clients with dementia to facilitate their daily activities and provide support when they are distressed. However, the nature of the care demands the practitioner’s active engagement, which becomes difficult to deliver as the number of people who need care expands. Researchers have been investigating the efficacy of developing agents that assume conversational tasks to alleviate this work. To contribute to the future design of agents for caregiving, we collected and analyzed ten conversations between clients with mild dementia and practitioners who provide care. Our analyses of turn-taking dynamics and dialogue acts …

An Integrated Theoretical Socio-Technical Framework For Implementing Service Robots’ Integration In Healthcare, Sujatha Alla

Engineering Management & Systems Engineering Theses & Dissertations

Healthcare workers, either clinical or non-clinical, are obligated to serve patients. However, lack of a sufficient number of professionals leads to burnout, severe stress, and, consequently, decreased quality of services. In this context, very few countries have been successful in employing service robots to perform dull, dirty, and/or dangerous tasks related to patient wellbeing/healthcare, while most countries are still skeptical about it. As robotics advances, there is an opportunity for healthcare to take advantage of this technology to reduce personnel workload and to reduce the possibility of exposure to contagious pathogens. However, healthcare is a vulnerable environment and requires critical …

A Framework For Human Evaluation Of Large Language Models In Healthcare Derived From Literature Review, Thomas Yu Chow Tam, Sonish Sivarajkumar, Sumit Kapoor, Alisa V Stolyar, Katelyn Polanska, Karleigh R Mccarthy, Hunter Osterhoudt, Xizhi Wu, Shyam Visweswaran, Sunyang Fu, Piyush Mathur, Giovanni E Cacciamani, Cong Sun, Yifan Peng, Yanshan Wang

Faculty, Staff and Student Publications

With generative artificial intelligence (GenAI), particularly large language models (LLMs), continuing to make inroads in healthcare, assessing LLMs with human evaluations is essential to assuring safety and effectiveness. This study reviews existing literature on human evaluation methodologies for LLMs in healthcare across various medical specialties and addresses factors such as evaluation dimensions, sample types and sizes, selection, and recruitment of evaluators, frameworks and metrics, evaluation process, and statistical analysis type. Our literature review of 142 studies shows gaps in reliability, generalizability, and applicability of current human evaluation practices. To overcome such significant obstacles to healthcare LLM developments and deployments, we …

Ai In The Health Professions, Heidi Monroe, Carrie Fry, Phillip Baker, Erika Busz

AI and the Future of Work

The aim of this track is to provide health professionals, and those interested in mental health and healthcare careers with an understanding of key aspects of AI use in healthcare. Participants will explore advantages of some recent AI developments and evaluate how they may be effectively leveraged to improve patient care, while addressing potential challenges, limitations, and ethical considerations.

Review Of Data Bias In Healthcare Applications, Atharva Prakash Parate, Aditya Ajay Iyer, Kanav Gupta, Harsh Porwal, P. C. Kishoreraja, R. Sivakumar, Rahul Soangra

Physical Therapy Faculty Articles and Research

In the area of medical artificial intelligence (AI), data bias is a major difficulty that affects several phases of data collection, processing, and model building. The many forms of data bias that are common in AI in healthcare are thoroughly examined in this review study, encompassing biases related to socioeconomic status, race, and ethnicity as well as biases in machine learning models and datasets. We examine how data bias affects the provision of healthcare, emphasizing how it might worsen health inequalities and jeopardize the accuracy of AI-driven clinical tools. We address methods for reducing data bias in AI and focus …

Multimodal Mixing Convolutional Neural Network And Transformer For Alzheimer’S Disease Recognition, Junde Chen, Yun Wang, Adnan Zeb, M. D. Suzauddola, Yuxin Wen

Engineering Faculty Articles and Research

Early recognition of Alzheimer’s disease (AD) and its precursor state, mild cognitive impairment (MCI), is pivotal in interrupting the progression of the disease and providing suitable treatment. Recent development in deep learning techniques has drawn great research attention for improving the efficacy of AD recognition. However, numerous current methods solely utilize data from a single auxiliary domain, limiting their ability to harness valuable intrinsic insights from multiple domains. To cope with the challenge, this paper is devoted to establishing an innovative multimodal medical data fusion model, termed as MMDF, to perform Alzheimer’s disease recognition. Multimodal data including clinical records and …

Assessing The Accuracy And Utility Of Chatgpt Responses To Patient Questions Regarding Posterior Lumbar Decompression, Alec Giakas, Rajkishen Narayanan, Teeto Ezeonu, Jonathan Dalton, Yunsoo Lee, Tyler Henry, John Mangan, Gregory Schroeder, Alex Vaccaro, Christopher Kepler

Department of Orthopaedic Surgery Faculty Papers

Aim: To examine the clinical accuracy and applicability of ChatGPT answers to commonly asked questions from patients considering posterior lumbar decompression (PLD). Methods: A literature review was conducted to identify 10 questions that encompass some of the most common questions and concerns patients may have regarding lumbar decompression surgery. The selected questions were then posed to ChatGPT. Initial responses were then recorded, and no follow-up or clarifying questions were permitted. Two attending fellowship-trained spine surgeons then graded each response from the chatbot using a modified Global Quality Scale to evaluate ChatGPT’s accuracy and utility. The surgeons then analyzed each question, …

Artificial Intelligence In Orthopaedic Education: A Comparative Analysis Of Chatgpt And Bing Ai’S Orthopaedic In-Training Examination Performance, Clark Chen, Vivek Biololikar, Duncan Vannest, James Raphael, Gene Shaffer

Einstein Health Papers

Background: This study evaluated the performance of generative artificial intelligence (AI) models on the Orthopaedic In-Training Examination (OITE), an annual exam administered to U.S. orthopaedic residency programs. Methods: ChatGPT 3.5 and Bing AI GPT 4.0 were evaluated on standardised sets of multiple-choice questions drawn from the American Academy of Orthopaedic Surgeons OITE online question bank spanning 5 years (2018–2022). A total of 1165 questions were posed to each AI system. The performance of both systems was standardised using the latest versions of ChatGPT 3.5 and Bing AI GPT 4.0. Historical data of resident scores taken from the annual OITE technical …